Convolutional sequence to sequence learning

由 J Gehring 著作 · 2017 · 被引用 3892 次 — We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can ...

2021年1月28日 — It computes an element-wise product of the weights of each word, multiplied by the weights assigned to the convolutional filter.The resultant ...

2017年8月6日 — The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural ...

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks.

This work introduces an architecture based entirely on convolutional neural networks, which outperform the accuracy of the deep LSTM setup of Wu et al.

2017年10月2日 — 論文出處: Convolutional Sequence to Sequence Learning.